In the engagement and intercept of a hostile ballistic missile (BM), a weapon system will typically track the ballistic missile objects using a ground-based radio frequency (RF) sensor and transmit track information to an intercepting missile for processing. The intercepting missile is typically equipped with an infrared (IR) sensor which is used to acquire and track the hostile ballistic missile objects as part of the engagement sequence.
Hostile ballistic missiles, however, can be expected to deploy decoy objects to conceal the warhead(s) and avoid tracking and interception. Multiple objects from one or more BM launch events may therefore be present in the IR sensor field of view (FOV). In order to select and guide to the targeted object, a correlation process takes place in order to relate the RF track information to the IR track information. The information from the RF sensor may contain object states as well as identifying properties or features for each object. Processors on board the interceptor missile take this information and relate it with its own measurements and processing to match the RF tracks on each object with the IR tracks and determine the object of interest for the engagement. The accuracy of this correlation process is extremely important to the success of the intercept. The determination of the object of interest will drive missile guidance algorithms to complete the intercept. However, uncertainties in both the RF and IR tracks can lead to poor correlation and degraded intercept performance.
A correlation process may produce inaccurate results due to a number of reasons. For example, information from each sensor may be corrupted by errors including random noise and translational and rotational biases. Measurement noise and systematic errors thus need to be minimized in order to properly correlate, track and intercept the target of interest. A correlation system and method should choose an optimal solution by modeling and solving for the object states and deterministic errors and accounting for the uncertainty and error distribution associated with each quantity. However, current implementations of correlation processes are based on ad hoc methods that may differ widely depending on the scenario and do not properly account for the deterministic biases associated with RF and IR measurements and the statistical properties of the estimated parameters.
Alternative systems and methods are desired.